CVE-2022-21740 in Tensorflow
Summary
by MITRE • 02/03/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable to a heap overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/06/2025
The vulnerability identified as CVE-2022-21740 affects TensorFlow, a widely-used open source machine learning framework that powers numerous artificial intelligence applications across industries. This heap overflow vulnerability specifically resides within the `SparseCountSparseOutput` implementation, which is part of TensorFlow's sparse tensor operations. The flaw represents a critical security concern as it could potentially allow attackers to execute arbitrary code or cause system instability when processing maliciously crafted sparse tensor inputs. The vulnerability impacts multiple versions of TensorFlow including 2.5.3, 2.6.3, 2.7.1, and the affected versions that would receive the fix in TensorFlow 2.8.0.
The technical nature of this heap overflow stems from improper bounds checking within the `SparseCountSparseOutput` function implementation. When TensorFlow processes sparse tensor operations involving count operations with sparse outputs, the code fails to validate input parameters adequately before performing memory allocation operations. This deficiency allows an attacker to provide malicious input that causes the program to write beyond allocated memory boundaries, potentially corrupting adjacent memory regions and creating opportunities for code execution. The vulnerability manifests during the processing of sparse tensor data structures where the algorithm calculates counts for sparse output representations without sufficient input validation mechanisms.
The operational impact of this vulnerability extends across various machine learning deployment scenarios where TensorFlow is utilized for processing large-scale sparse data. Organizations relying on TensorFlow for recommendation systems, natural language processing, or any application involving sparse tensor operations face potential security risks. Attackers could exploit this vulnerability through crafted inputs in machine learning pipelines, potentially leading to remote code execution on systems running affected TensorFlow versions. The widespread adoption of TensorFlow in production environments means that this vulnerability could affect numerous applications across different sectors including finance, healthcare, and technology services.
Mitigation strategies for CVE-2022-21740 involve immediate deployment of the patched TensorFlow versions, with the fix being cherry-picked for the affected supported releases. Organizations should prioritize updating to TensorFlow 2.8.0 or the corresponding patched versions for 2.7.1, 2.6.3, and 2.5.3 to eliminate the heap overflow risk. Additionally, implementing input validation measures and monitoring for anomalous tensor processing patterns can provide defensive layers. The vulnerability aligns with CWE-121, heap-based buffer overflow, and represents a potential entry point for attackers following ATT&CK technique T1059.1001 for command and scripting interpreter execution. System administrators should also consider implementing network segmentation and access controls around machine learning infrastructure to limit potential exploitation scope.
The remediation process requires careful testing of patched versions to ensure compatibility with existing machine learning workflows while maintaining the security benefits. Organizations should conduct thorough regression testing of their TensorFlow-based applications to verify that the security patch does not introduce performance degradation or functional issues in their deployed systems. Regular vulnerability scanning and monitoring of TensorFlow component usage within organizational environments will help identify and remediate similar security concerns proactively. The vulnerability serves as a reminder of the importance of security considerations in machine learning frameworks, particularly when handling complex data structures like sparse tensors that require careful memory management and input validation.